Simultaneous optimization of surface roughness and material removal rate for turning of X20Cr13 stainless steel

Abstract

The objective of this article is to manufacture low-cost, high-quality products with maximum productivity in short time. In this work, four stages are considered: statistical investigation of the experimental results based on ANOVA, modelling based on regression analysis and mono- and multi-objective optimizations. In the first stage, turning experiments were carried out using an orthogonal array (L16) of Taguchi. Effects of cutting parameters on surface roughness and material removal rate were determined using ANOVA and interaction plots. In the second stage, regression analysis was utilized to formulate second-order models of all data gathered in the experimental works; these models could be used to predict responses in turning of X20Cr13 steel with a minor error. In the third stage, responses were used alone in an optimization study as an objective function. To minimize all responses, Taguchi’s signal-to-noise ratio was used. In the fourth stage, responses were optimized simultaneously using grey relational analysis.